We can distinguish between two major settings based on the nature of the use-case and participant/client type [1]}. In cross device learning the target is a very large amount of stateless, low-powered clients engaging in relatively cheap, short bursts of computation. Clients are typically mobile or IoT devices and data is partitioned horizontally.
For cross-silo use-cases, clients are larger entities with more local computational power and storage capability. Data can be partitioned horizontally or vertically. In cross-device learning, communication overhead and handling failed connections are key challenges, whereas cross-silo learning can be both computationally and communication constrained and models are relatively large (MBs to GBs). FEDn aims to support the requirements across both these axes of federated learning.
